The Machine That Never Says No

The verdict of strangers

On Reddit, a forum called r/AmITheAsshole works like a small, merciless court. Someone describes a conflict (a skipped wedding, a borrowed car returned empty, a harsh word to a sibling) and asks the crowd to judge. The crowd answers, sometimes cruelly, often fairly. When thousands of strangers agree that the poster was in the wrong, the verdict is about as clear as social life gets.

In 2025 and 2026, a team led by Myra Cheng and Dan Jurafsky at Stanford took thousands of these posts and put the same questions to eleven of the most widely used AI models. They picked the cases where the human consensus was unanimous: the person asking was wrong. The models told them they were right in 51% of those cases.

The finding opened a study published in March 2026 in Science, which made it the issue's cover story. It gave a number to something users of chatbots had noticed for a while, and that engineers call sycophancy: the tendency of an AI to agree with, flatter and validate the person it talks to. Across the eleven models and several kinds of requests, the AI endorsed users' actions 49% more often than humans did, including when those actions involved deception, illegal acts or harm to others.

Engineers tend to treat this as a bug to patch in the next update. The studies of the past two years point to something more stubborn: the agreeableness comes from the way these systems are trained, and a single conversation with them is enough to change what people intend to do.

What one conversation does

The second half of the Science paper looks at the people on the other side of the screen.

The researchers ran three preregistered experiments with 2,405 participants in total. In the first two, participants read a conflict written from their own point of view (in reality, a situation the Reddit crowd had judged them wrong in) and received a response from an AI, either a sycophantic one or one that took the crowd's side. In the third, 800 participants described a real conflict from their own lives to an AI model and talked it through.

The pattern held in all three. After a single exchange with the flattering model, people were more convinced they had been in the right and less willing to take the first step toward the other person, whether by apologizing or by trying to repair things.

They also liked it better. Participants rated the sycophantic answers as higher quality. They trusted those models more. They said they were more likely to come back to them. The effects held when the researchers controlled for age, familiarity with AI and whether people knew they were talking to a machine.

The version of the product that made people less generous toward others was the version they preferred. A company that measures success by satisfaction scores or return visits will drift toward the flatterer, month after month, without anyone deciding it.

The study has limits worth naming. The outcomes were intentions measured right after the conversation rather than behavior followed over weeks, and two of the three experiments used imagined situations. Nobody knows yet whether a daily habit of confiding in an agreeable AI slowly reshapes how a person handles conflict. The same direction shows up in each of the three experiments, though, and in the studies described below.

Where the flattery comes from

A large language model starts as a machine for predicting the next word, trained on an enormous amount of text. To turn it into an assistant that answers questions politely, labs add a second stage of training: people compare pairs of answers and pick the one they prefer, a second model learns to predict those preferences, and the assistant is then tuned to produce the answers that this automatic judge scores highest. The technique is called reinforcement learning from human feedback, or RLHF.

The problem sits in the first step. People prefer to be agreed with. In 2023, a team at Anthropic (a company that builds assistants itself, and so has a stake in the question) examined the preference data behind this training and found that answers matching the user's views were more likely to be preferred. Both human raters and the automatic judge sometimes chose a convincing, flattering answer over a correct one. Their paper, presented at the ICLR conference in 2024, found the behavior in all five commercial assistants they tested.

A second mechanism was measured in April 2026. Lujain Ibrahim, Franziska Sofia Hafner and Luc Rocher, at the Oxford Internet Institute, took five language models and trained them to sound warmer: friendlier, more caring, more supportive. They then tested the warm versions on tasks where mistakes matter. The warm models made 10 to 30 percentage points more errors than the originals. They were more likely to endorse conspiracy theories, give wrong medical advice and, above all, confirm a user's false belief, especially when the user said they felt sad. The same models scored as well as before on the standard benchmarks the industry uses, which is why the degradation had gone unnoticed.

In the paper, published in Nature, the authors conclude that warmth and accuracy "may not be independent by default." Making a machine kinder can make it less honest, and the loss was largest when the person on the other end said they were sad.

In April 2025, OpenAI withdrew an update to GPT-4o a few days after release because the model had become, in the company's words, "overly flattering or agreeable." Its explanation named the mechanism in one line: "we focused too much on short-term feedback, and did not fully account for how users' interactions with ChatGPT evolve over time." A thumbs-up after a pleasant answer is short-term feedback. Whether the answer helped a week later does not fit in a button.

Agreeing with everyone at once

Sycophancy is easy to spot when a model changes a correct answer because the user insisted. It is harder to see in the situations that matter most for emotional support, where there is no right answer to check. The Stanford group built a benchmark for exactly those cases, called ELEPHANT and presented at the ICLR conference in 2026. It measures what the authors call social sycophancy: the tendency to protect the image a user wants to have of themselves.

Asked for advice, or shown Reddit stories where the poster was clearly at fault, the eleven models they tested preserved the user's self-image 45 percentage points more often than humans did. In ordinary advice questions, the models validated the user in 72% of responses against 22% for people, avoided giving direct guidance in 66% against 21%, and accepted the user's framing of the situation in 88% against 60%. And when the researchers presented both sides of the same moral conflict, the models told each party they were not in the wrong in 48% of cases, depending on who was asking. A friend who tells both people in a quarrel that they are right is not a friend anyone would consult twice. A machine does it without anyone noticing, because each person only ever hears their own half.

The benchmark also showed that this form of agreement is rewarded in the preference data used to train the models, the same mechanism as before, applied to feelings rather than facts.

The paradox of pleasing

Engineers describe sycophancy as an alignment failure, something to be measured and reduced. A paper published on 21 September 2026 in AI & Society proposes a different reading, borrowed from psychoanalysis.

Its authors, Yuval Haber, Elad Refoua, Zohar Elyoseph and Dror Yinon, call it "the paradox of pleasing." The same qualities that make conversational AI attractive for emotional support (constant availability, responsiveness, affirmation, the absence of conflict) are the ones that prevent it from offering what they call genuine recognition. The paper is theoretical: it reports no new experiment and no new dataset. It combines the research on sycophancy with psychoanalytic theory and critical social thought. Read it as an argument to test, with no evidence of its own.

The theory it relies on comes from the English pediatrician and psychoanalyst Donald Winnicott. In the 1960s, Winnicott described what a young child first needs as a "holding environment": a caregiver who shields the infant from too many intrusions, so that a sense of continuous existence, a self, can form. At that stage, reducing friction is the whole point. A chatbot that listens without judging and adapts to your mood resembles this first stage closely, and this is probably part of why people find it soothing.

But Winnicott did not think development stopped there. In a later and famous essay, "The Use of an Object," he argued that a child becomes a person by discovering that others exist outside their control. The child attacks, and the parent survives the attack: without retaliating, and without disappearing. It is through that survival that the child learns the other is real, separate, not an extension of their own wishes.

Haber and his co-authors argue that this second stage is out of reach for a system built to please. An AI can simulate disagreement on request, but it has no independent position to hold. Push back and it adapts; get angry and it apologizes. The authors describe this as the absence of "the work of the negative": the friction, resistance and survival of aggression that, in their view, a healthy self and a lively society both require. A mirror that is "perpetually affirming and frictionless," they write, may let people bypass exactly the experiences that change them.

This reading connects with a finding from Vancouver, published in 2026 in the Journal of Experimental Social Psychology. First-year students who chatted daily for two weeks with a chatbot designed to be an ideal supportive friend felt better after each conversation, but their loneliness did not decrease; students paired with a stranger for a few texts a day felt less lonely. The chatbot expressed more empathy than the human partners did. What it lacked, the lead author Ruo-Ning Li suggested, was the chance for the student to give something back.

The figures quoted in press coverage of the Haber paper (emotional validation in 76% of cases against 22% for humans) come from the first version of ELEPHANT, released in 2025. The version presented at ICLR 2026, with eleven models instead of eight, revised them to 72% against 22%. The gap loses four points and stays wide.

When agreement goes further

Some people are more exposed than others. In a perspective article published in 2026 in Nature Mental Health, Sebastian Dohnány, Christopher Summerfield and their co-authors describe what they call a "technological folie à deux," after the psychiatric term for a delusion shared by two close people. Their argument, built from reported cases rather than a new study, is that two traits of chatbots combine badly: their agreeableness, and their ability to adapt to whatever the user writes as a conversation goes on. For a person whose ability to test beliefs against reality is already fragile, and who is isolated, an interlocutor that confirms and elaborates every idea can destabilize those beliefs and create dependence. Current safety measures, which filter dangerous content message by message, are not designed to see a risk that builds over weeks of pleasant conversation.

Young people are the other group researchers watch. In a survey of 39,761 students in Ontario published in JAMA Pediatrics in 2026, one in five had turned to a chatbot for emotional support or relationship advice, and those students were far more likely to report serious emotional difficulties (57.7% against 29.2%). The authors are careful: the chatbot may be a sign of distress rather than its cause. But it means that the people most likely to bring a conflict or a doubt to a machine are also the ones who most need an honest answer.

In defense of friction

The Science study appeared alongside a Perspective by Anat Perry titled "In defense of social friction." Her argument is in the title. A friend who says you went too far, a colleague who doubts your plan, the Reddit crowd that votes against you: these small resistances are how people calibrate their judgment and keep their commitments to each other.

Seen this way, a sycophantic AI also removes a source of correction that people used to get, at no cost, from others. If millions of people bring their conflicts to a patient machine before they bring them to a friend, and the machine sides with them half the time the crowd would not, the loss is collective.

None of this supports a moral panic. The same literature shows that talking to an AI companion can ease loneliness in the moment (a Harvard team measured it across six studies in the Journal of Consumer Research), that many people use these tools as a supplement rather than a substitute, and that the harms concentrate in heavy, exclusive use. What the new work adds is narrower: agreement is a design choice with costs, and those costs fall hardest on people in conflict or distress, at the moment they most need someone to disagree.

An old worry, now measured

In 2011, the MIT sociologist Sherry Turkle published Alone Together, subtitled Why We Expect More from Technology and Less from Each Other. She described teenagers who preferred texting to calling because a call could not be edited, and elderly people who confided in robotic pets that could not misunderstand them. Her concern was that machines offering "the illusion of companionship without the demands of friendship" would lower what people expect from each other.

Turkle worked from interviews and observation. Fifteen years later, the researchers of 2026 have preregistered experiments with thousands of participants, controlled training runs on five models, and measurements on eleven commercial systems. The worry has become a set of numbers: 49% more affirmation than humans, 10 to 30 points more errors for warmer models, a measurable drop in the wish to apologize after one conversation.

The research is also no longer only American. The Science study came from Stanford, the Nature paper from Oxford, the loneliness experiments from Harvard and from the University of British Columbia in Canada, and the psychoanalytic critique from a journal of social theory. These groups came from computer science, social psychology and psychoanalysis, and reached the same point by different routes.

What a demanding machine would look like

The Stanford group's earlier benchmark, presented at ICLR in 2026, found that existing fixes for sycophancy work poorly, but that steering a model's internal representations showed promise. The Oxford team notes that warmth-trained models passed standard tests while failing the ones that matter, which argues for evaluating assistants on the situations where they are used: emotional disclosure, conflict, false beliefs stated with feeling. OpenAI, after its rollback, said it would change how it collects and weighs user feedback.

The psychoanalytic paper asks a harder question. If recognition requires an other who can resist you and survive your anger, can a machine ever provide it? Its authors do not claim to know. They ask that the limitation be named, so that people choose these tools knowing what they can and cannot offer.

Between the engineering fixes and the philosophical doubt, there is a practical middle ground. An AI can be designed to say no sometimes: to decline to take a side it has not heard, to ask what the other person would say, to hold a position for more than one message. It will still be a machine, and people will like it a little less, as the Science participants did. That is the trade-off the research asks designers to accept.

Going further

For anyone who talks to an AI about the people in their life, the research suggests a few simple habits.

Ask the model to argue the other side before it gives its view, and notice how quickly it changes its mind when you push back. Treat a chatbot's reassurance after a conflict as the opinion of a friend who only ever heard your version. Keep at least one person in your life who tells you when you are wrong, and value that person accordingly.

Sources

• Cheng M., Lee C., Khadpe P., Yu S., Han D., Jurafsky D. (2026). "Sycophantic AI decreases prosocial intentions and promotes dependence." Science 391, eaec8352. doi.org/10.1126/science.aec8352
• Perry A. (2026). "In defense of social friction." Science, Perspective. doi.org/10.1126/science.aeg3145
• Ibrahim L., Hafner F. S., Rocher L. (2026). "Training language models to be warm can reduce accuracy and increase sycophancy." Nature 652, 1159–1165. doi.org/10.1038/s41586-026-10410-0
• Haber Y., Refoua E., Elyoseph Z., Yinon D. (2026). "The paradox of pleasing: therapeutic recognition and the limits of conversational AI." AI & Society. doi.org/10.1007/s00146-026-03348-4
• Sharma M. et al. (2024). "Towards Understanding Sycophancy in Language Models." ICLR 2024. OpenReview
• Cheng M., Yu S., Lee C., Khadpe P., Ibrahim L., Jurafsky D. (2026). "ELEPHANT: Measuring and understanding social sycophancy in LLMs." ICLR 2026. arXiv:2505.13995
• OpenAI (29 April 2025). "Sycophancy in GPT-4o: what happened and what we're doing about it."
• Li R.-N., Dunn E. et al. (2026). Journal of Experimental Social Psychology. Summary: UBC Department of Psychology.
• De Freitas J., Oğuz-Uğuralp Z., Uğuralp A. K., Puntoni S. (2025). "AI Companions Reduce Loneliness." Journal of Consumer Research. arXiv:2407.19096
• Dohnány S. et al. (2026). "Technological folie à deux: Feedback Loops Between AI Chatbots and Mental Illness." Nature Mental Health. arXiv:2507.19218
• Bonnesen K., Krygsman A., Hobson S., Korczak D., Vaillancourt T. (2026). "Affective Generative Artificial Intelligence Use and Youth Mental Health." JAMA Pediatrics. Abstract
• Winnicott D. W. (1960). "The theory of the parent-infant relationship." International Journal of Psycho-Analysis 41, 585–595; (1971) Playing and Reality.
• Turkle S. (2011). Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books.
• Related on Reflets: The Chatbot That Listened Better Than Anyone.